There is a large body of literature that addresses the \(\mu \) AGC design. These design schemes can be generally categorized into the centralized, distributed, and decentralized control [1]}. The centralized \(\mu \) AGC relies on a monopolistic decision-making platform that gathers measurements from the DERs under control, computes regulation signals, and dispatches these signals to the DERs [2]}, [3]}. The distributed \(\mu \) AGC employs cooperative control policies among neighboring DERs to achieve certain objectives. It can be further classified into three sub-groups: averaging-based methods [4]}, distributed-consensus-based methods [5]}, and event-triggering-based methods [6]}.
In decentralized \(\mu \) AGC design, each DER participating in the \(\mu \) AGC recovers its own nominal frequency to a nominal value independently. The washout filter is investigated in [7]} and shown to be equivalent to a decentralized secondary control. In [8]}, a decentralized LQR control design is proposed to achieve frequency restoration in an optimal way. Another idea to achieve decentralized frequency regulation is based on \(\mu \) AGC. The estimation-based \(\mu \) AGC [9]} employs the system states estimated locally instead of the true measurements to generate control signals.
